Category Archives: Get to Know a Stat

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Welcome to Get to Know A Stat-where we take a new fangled fancystat of some kind and explain it in layman’s terms as best we can. In honor of our “bullpen week” series this week, we’re starting off with a pair of pitching stats that Court (@Rcourtswift) uses in some of his previews. He’s all fancy like that and helped me to write a bit of this below.

While we’ve danced around a lot of stats and given you the skinny on some of the new ones, today I bring you your first true taste from the mountain top. The Book (no not that “The Book” this is a baseball book called The Book) purported to prove many of the “common knowledge” unwritten strategies of baseball. That is they wanted to make a book of “how to play by the book.” The twist, of course, is that rather than just write all these things down, they did the math (a LOT of math) to see if many of these things (say…I dunno…the sac bunt?) actually worked the way people thought it did.

The answer quite often is “no,” or at least “probably not.” I’m not going to chase down many of those notions today (you should buy The Book and read it for yourself) but I do want to talk about one of the jewels of the book, and how it relates to at least one of the new stats being tossed around.

RE24 and REW both use in the 24 base/out states of baseball, and the scoring probabilities associated with them. That’s our first stop. You’re gonna want to pay attention here (and maybe grab a pen and paper)

Okay now that THAT joke is out of the way we can just comment briefly about how the song is named after the original title of Tolstoy’s War and Piece. (Right about 2:00).

– The mere mention of the advanced stat is enough to divide a room of baseball fans the way the words “Gun Control” might divide the halls of Congress.

On the one hand is the idea that this “end all be all” stat should be the deciding factor in nearly all baseball decisions. On the other, the vomitous rejection that a baseball player’s full value can be wrapped up in one singular number.

The answer, as is the case in nearly all of these types of divisive debates, is actually somewhere in between. With the New Year, we’ll be looking at different parts of this stat to shed some light on why many love it, and why other’s hate it. By the end, I hope you’ll see it for what I think it is-a fairly good short hand for ranking and comparing players across positions, leagues and teams.

Disclaimer

Today’s post is just to warm you up to the idea of WAR, what it is and, frankly, give you just enough information to get in trouble with. So don’t go proclaiming yourself a WAR expert by the end of this post (and, alternatively, don’t start yelling at me because the the post is incomplete/misleading/wrong): It’s not meant to be everything there is to know. We’ll do follow up posts looking at some of the finer detail of what goes into the stat, and by the end you’ll (hopefully) be in a better position to make up your mind about how much (if any) stock to put into it.

Last thing: WAR for position players and hitters is different than WAR from pitchers. Lots of folks don’t like WAR for pitchers (See Comments from Court’s Post from Yesterday.)

*Also, different places (Baseball Reference, Fangraphs, Front Offices) calculate WAR differently. I’m just focusing on hitters in the Fangraphs WAR formula for now (fWAR).

“My favorite stat right now and always has been the stat of hitting with runners in scoring position… [B]atting average and on-base percentage and all of those things are great, but who is doing damage and how can they hit with guys in scoring position? I don’t know if I can help guys with that, but that’s the stat that I’m most concerned with, that I like the most. So do, I care whether a guy’s hitting .250 as opposed to .280? No, I care whether he can drive a run in.” Matt Williams

Oh Faux Pas! In the course of two sentences quotes Matt Williams just used the wrong soup spoon at the table of the advanced statistics crowd. If this were other walks of life, Matt Williams might have essentially just said:

That the Star Wars movies are his favorite movies of all time…particularly Episode One… where Vader is a kid.

That he is a total foodie, and the Big Mac is his favorite sandwich

He loves craft beers. Especially anything made by Miller or Budweiser.

Comes off a little snobby? Probably. But still, he did just walk into a room of hipsters wearing a Nickelback shirt… un-ironically. So let’s take a look at just what folks find so objectionable about Batting Average with Runners in Scoring Position.

Welcome to Get To Know a Stat. This week Court (@RCourtSwift) takes a hack at explaining UZR and how it applies to the Nationals Center Fielder.

Defense. It’s vitally important to a team, but almost impossible to discuss amongst fans. Diving catches look great-but did a guy make a great play, or was he just late getting to a ball he should have had easily? Was a double in a gap something that should have been a single? Or an out? Apart from the errant throw or bobble most fans (myself included) don’t really keep track of what is going on defensively on the field. Court is going to help us out.

This week’s stat is Ultimate Zone Rating, or UZR. It’s the top metric for computing how many runs above replacement (how much better a player is than a standard AAA scrub) a player’s defense contributes. Defense is the last frontier of advanced metrics and this stat is often used as an example of all that is wrong with “the new school” of statistics. While it is true that this stat is not the best, it is the best we have to this point.

UZR is one of the more theoretical stats because it does not measure what happened, instead it assigns a value to each type of batted ball then either gives credit or deduct points from those players surrounding the play. It then compares that player to all other players at the same position, over a 6 year period. The final number represents how much better, or worse, a player’s defensive compared to his peers.

Welcome to Get to Know a Stat! Once a Week (or so) I intend to take an advanced baseball statistic and present it to you in a way that’s understandable.

This week, I want to look at a stat called Weighted On Base Average, something mentioned by Court a few weeks ago in his Holding Court column. A lot of folks hold on to batting average as the end all/be all of comparing batters despite the fact that there are many other metrics to look at-often giving a more complete picture of what is going on.

To give you a basis for what I’m talking about, allow me to parrot some of the more insane arguments I’ve heard/read to start Steve Lombardozzi over Danny Espionsa at second base. Note: [Insert my usual disclaimer of love for Lombo as a utility player even if I disagree with the position he should be a starter]. The case for Steve Lombardozzi goes something like this:

He’s just as good a fielder as Danny Espinosa [Not at all true, but we’ll deal with that another day]

Danny Espinosa strikes out too much, and Steve Lombardozzi doesn’t strike out nearly as much

About a week ago Danny Espinosa was only batting .155 (currently .185) and Lombardozzi was batting .365 (now .235)

Danny Espinosa can’t hit with runners in scoring position, and gets no RBIs. Lombo is “scrappy” and “clutch.”

Now usually, I can go through the whole litany of reasons that is insane.

You can start with the fact that Lombo hasn’t nearly had the plate appearances Espinosa has had (so it’s likely that his average will drop-which it did recently).

You can point out that while Lombo doesn’t strikeout as much as Espinosa, he doesn’t draw walks (he has only one) which indicates maybe he doesn’t have a great eye, but just makes contact outside the zone (bad contact that leads to ground outs).

You can also take a look at Total bases which is the total number of bases a player gets per hit (a HR is 4, Triple 3, Doubles 2, Singles 1). Espinosa’s total bases double that of Lombardozzi’s- meaning he’s getting much bigger hits than Lombo, who hits a lot of dribbler singles that squeak through.

You can do all that, and I can do all that, but it might be better to look at something come up with by Tom Tango called Weighted On Base Average (wOBA).

Inspired by our podcast with @ouij, and yesterday’s follow-up regarding the Pythagorean Win Expectancy, math-lover Jared Kobe @SCviaDC offers the following deeper look at the math of Pythag. I’ve interjected my notes in red just to make sure we are all following along.

The basic question Jared asks is how good has this Pythagorean Win Expectancy been over the course of Baseball History? My look at just 2012 is fraught with room for error. By looking at more seasons (all of them) and looking at some better statistical indicators, we can get a better picture of just how good Pythag is/isn’t.

Analysis

The Pythagorean Win Expectancy formula produces the probability that a team will win a baseball game. It does so by essentially dividing 1 by 1 plus the square of the rate you give up runs for each run you score. To illustrate what this rate means, take the 2012 Nationals who gave up 594 runs and scored 731. This gives us a rate of .812 runs allowed per runs scored, meaning the Nats allowed 13 runs for every 16 they scored. That’s the number/fraction that gets squared and added to 1, which finally gets divided into 1 to give you the probability of winning.

Baseball Reference has since revised the original formula to better fit the historical data. Instead squaring the runs allowed per runs scored rate, they take the rate to the power of 1.83 (approximately the square root of 3.35). I included the revised formula in my calculations for comparison sake.

I looked into how Bill James came to derive the formula from Frank’s link, and he based this win probability by putting runs scored on a Weibull distribution. From there, I tried to understand how the rest the actual formula was derived… but let’s just say that Weibull distribution is much more fun to say than it is to try to calculate or understand. Note: So we won’t be trying that.

So, without a function that calculates the probability to compare the data points against, we are stuck with looking at how off the calculated expected wins were from the actual wins to judge how well the Pythagorean Win formula works. I used Baseball Reference to get the Wins, Losses, Runs Scored, and Runs Allowed for each team, ever. I ignored games listed as “Ties” (mainly because it seems those games were essentially rain-outs made up later in the season.) I then calculated the Pythagorean Wins using the formula and multiplying it by the total of wins and losses (games played).So just like yesterday’s post, but you know…with every team in every season ever.

Now, determining the number of expected wins can be a little tricky from a comparison standpoint. Baseball teams win games in discrete numbers; multiplying the number of games in a season by a probability will rarely, if ever, give you a whole number. Therefore, there is a problem with rounding the expected win total, because having .2 wins is a little weird. Frank ignored this issue in his post, which is fine because simple is usually best. And I didn’t want to make the wrong choice. I tried rounding up to next number, rounding down to the next number, and simply rounding to the nearest integer. From this, I would guess that when James (and later Baseball Reference) modeled the formula, they rounded to the nearest integer.

The difference between the Actual Wins and the Expected Wins was calculated by subtracting the Expected Wins from the Actual Wins, so a negative number is “underperforming” and a positive number is “overperforming” based on the Expected wins. Note: Again, same as I did yesterday.

On Monday’s podcast, Ouij came on the show to talk about his model and method for predictive analysis. Specifically, we discussed the Pythagorean Win/Loss Expectation formula. After it was over, I wanted to know: Just how does it work, and more importantly… how well does it work?

It’s not much, but I decided to run the 2012 standings through the P-formula to see how well the expectations matched up to what happened. Some very surprising (and not surprising at all) results followed.